dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorYang, S. Y.
dc.creatorHo, S. L.
dc.creatorNi, G. Z.
dc.creatorMachado, Jose Marcio
dc.creatorWong, K. F.
dc.date2014-05-20T15:28:47Z
dc.date2016-10-25T18:03:59Z
dc.date2014-05-20T15:28:47Z
dc.date2016-10-25T18:03:59Z
dc.date2007-04-01
dc.date.accessioned2017-04-06T00:09:08Z
dc.date.available2017-04-06T00:09:08Z
dc.identifierIEEE Transactions on Magnetics. Piscataway: IEEE-Inst Electrical Electronics Engineers Inc., v. 43, n. 4, p. 1601-1604, 2007.
dc.identifier0018-9464
dc.identifierhttp://hdl.handle.net/11449/38540
dc.identifierhttp://acervodigital.unesp.br/handle/11449/38540
dc.identifier10.1109/TMAG.2006.892112
dc.identifierWOS:000245327200114
dc.identifierhttp://dx.doi.org/10.1109/TMAG.2006.892112
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/881644
dc.descriptionTo enhance the global search ability of population based incremental learning (PBIL) methods, it is proposed that multiple probability vectors are to be included on available PBIL algorithms. The strategy for updating those probability vectors and the negative learning and mutation operators are thus re-defined correspondingly. Moreover, to strike the best tradeoff between exploration and exploitation searches, an adaptive updating strategy for the learning rate is designed. Numerical examples are reported to demonstrate the pros and cons of the newly implemented algorithm.
dc.languageeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relationIEEE Transactions on Magnetics
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectgenetic algorithm (GA)
dc.subjectglobal optimization
dc.subjectinverse problem
dc.subjectpopulation based incremental learning (PBIL) method
dc.titleA new implementation of population based incremental learning method for optimizations in electromagnetics
dc.typeOtro


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